10+ TechMojo Solutions Interview Questions and Answers

No, decision trees in a random forest are different due to the use of bootstrapping and feature randomization.

• Decision trees in a random forest are trained on different subsets of the data through bootstrapping.

• Each decision tree in a random forest also considers only a random subset of features at each split.

• The final prediction in a random forest is made by aggregating the predictions of all individual decision trees.

Handling class imbalanced dataset involves techniques like resampling, using different algorithms, adjusting class weights, and using ensemble methods.

• Use resampling techniques like oversampling the minority class or undersampling the majority class.

• Try using different algorithms that are less sensitive to class imbalance, such as Random Forest or XGBoost.

• Adjust class weights in the model to give more importance to the minority class.

• Utilize ensemble methods like bagging or b...read more

Biasing is the error due to overly simplistic assumptions in the learning algorithm. Overfitting is when a model is too complex and fits the training data too closely, leading to poor generalization. Underfitting is when a model is too simple to capture the underlying structure of the data.

• Biasing occurs when a model has high error on both training and test data due to oversimplified assumptions.

• Overfitting happens when a model is too complex and captures noise in the trainin...read more

Deep learning neural networks are a type of artificial neural network with multiple layers, used for complex pattern recognition.

• Deep learning neural networks consist of multiple layers of interconnected nodes, allowing for more complex patterns to be learned.

• They are capable of automatically learning features from data, eliminating the need for manual feature engineering.

• Examples include Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks...read more

Neural network is a computational model inspired by the way the human brain works, used for machine learning tasks.

• Neural network is a series of algorithms that attempts to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

• Backpropagation is a technique used to train neural networks by updating the weights of the network to minimize the difference between the predicted output and the actual output.

• CNN (Convoluti...read more

Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to the all observations in actual class.

• Precision focuses on the accuracy of positive predictions, while recall focuses on the proportion of actual positives that were correctly identified.

• Precision = TP / (TP + FP), Recall = TP / (TP + FN)

• High precision means that when the model predicts a positive result...read more

RELU is an activation function used in neural networks to introduce non-linearity.

• RELU stands for Rectified Linear Unit.

• It is a simple function that returns the input if it is positive, and 0 otherwise.

• It is commonly used in deep learning models due to its simplicity and effectiveness.

• Other activation functions include sigmoid, tanh, and softmax.

I am looking for a competitive salary based on industry standards and my experience.

• Research industry standards for Data Scientist salaries

• Consider my level of experience and skills when determining salary expectations

• Be open to negotiation based on the overall compensation package offered

Stemming and lemmatization are techniques used in natural language processing to reduce words to their base or root form.

• Stemming is a process of reducing words to their base form by removing suffixes.

• Lemmatization is a process of reducing words to their base form by considering the context and part of speech.

• Stemming is faster but may not always produce a valid word, while lemmatization is slower but produces valid words.

• Example of stemming: 'running' -> 'run', 'jumps' -> 'j...read more

Multicollinearity can be measured using correlation matrix, variance inflation factor (VIF), or eigenvalues.

• Calculate the correlation matrix to identify highly correlated variables.

• Use the variance inflation factor (VIF) to quantify the extent of multicollinearity.

• Check for high eigenvalues in the correlation matrix, indicating multicollinearity.

• Consider using dimensionality reduction techniques like principal component analysis (PCA) to address multicollinearity.

Homoscedasticity refers to the assumption that the variance of errors is constant across all levels of the independent variable.

• Homoscedasticity is a key assumption in linear regression analysis.

• It indicates that the residuals (errors) have constant variance.

• If the residuals exhibit a pattern where the spread of points increases or decreases as the predicted values increase, it violates the assumption of homoscedasticity.

• This violation can lead to biased and inefficient estim...read more

Bias-variance trade off is the balance between underfitting and overfitting in machine learning models.

• Bias refers to the error introduced by approximating a real-world problem, leading to underfitting.

• Variance refers to the model's sensitivity to fluctuations in the training data, leading to overfitting.

• Finding the right balance between bias and variance is crucial for creating a model that generalizes well to unseen data.

• Regularization techniques like Lasso and Ridge regres...read more

Optimizers are algorithms used to adjust the parameters of a model to minimize the error between predicted and actual values.

• Optimizers are used in machine learning to improve the accuracy of models.

• They work by adjusting the weights and biases of a model during training.

• Common optimizers include Gradient Descent, Adam, and RMSprop.

• The choice of optimizer depends on the type of problem and the characteristics of the data.

• Optimizers can help models converge faster and avoid ge...read more

Poisson distribution is a probability distribution that expresses the likelihood of a given number of events occurring in a fixed interval of time or space.

• Describes the number of events that occur in a fixed interval of time or space

• Events are independent of each other

• Average rate of occurrence is constant

• Examples: number of emails received in an hour, number of customers arriving at a store in a day